Overview

Dataset statistics

Number of variables60
Number of observations260476
Missing cells9138039
Missing cells (%)58.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.2 MiB
Average record size in memory480.0 B

Variable types

Numeric21
Categorical38
Text1

Alerts

NPCJP1B has constant value ""Constant
NPCJP1C has constant value ""Constant
NPCJP1D has constant value ""Constant
NPCJP1E has constant value ""Constant
NPCJP1F has constant value ""Constant
NPCJP1G has constant value ""Constant
NPCJP1H has constant value ""Constant
NPCJP1I has constant value ""Constant
NPCJP1J has constant value ""Constant
NPCJP1K has constant value ""Constant
NPCJP1L has constant value ""Constant
NPCJP1M has constant value ""Constant
NPCJP1N has constant value ""Constant
NPCJP1A has constant value ""Constant
NPCJP1O has constant value ""Constant
NPCJP1P has constant value ""Constant
NPCJP1Q has constant value ""Constant
NPCJP9A has constant value ""Constant
NPCJP9B has constant value ""Constant
NPCJP9C has constant value ""Constant
NPCJP9D has constant value ""Constant
NPCJP9E has constant value ""Constant
NPCJP9F has constant value ""Constant
NPCJP9G has constant value ""Constant
NPCJP9H has constant value ""Constant
NPCJP9I has constant value ""Constant
NPCJP9J has constant value ""Constant
NPCJP8A has constant value ""Constant
NPCJP8B has constant value ""Constant
NPCJP8C has constant value ""Constant
NPCJP8D has constant value ""Constant
NPCJP8E has constant value ""Constant
NPCJP8F has constant value ""Constant
NPCJP8G has constant value ""Constant
NPCJP8H has constant value ""Constant
DIRECTORIO is highly overall correlated with DIRECTORIO_HOG and 1 other fieldsHigh correlation
DIRECTORIO_HOG is highly overall correlated with DIRECTORIO and 1 other fieldsHigh correlation
DIRECTORIO_PER is highly overall correlated with DIRECTORIO and 1 other fieldsHigh correlation
NPCJP10 is highly overall correlated with NPCJP9AA and 1 other fieldsHigh correlation
NPCJP11 is highly overall correlated with NPCJP12High correlation
NPCJP12 is highly overall correlated with NPCJP11High correlation
NPCJP2 is highly overall correlated with NPCJP3 and 1 other fieldsHigh correlation
NPCJP3 is highly overall correlated with NPCJP2High correlation
NPCJP6 is highly overall correlated with NPCJP2High correlation
NPCJP9AA is highly overall correlated with NPCJP10 and 2 other fieldsHigh correlation
NPCJP9AB is highly overall correlated with NPCJP9AAHigh correlation
NPCJP9AC is highly overall correlated with NPCJP9AEHigh correlation
NPCJP9AD is highly overall correlated with NPCJP9AAHigh correlation
NPCJP9AE is highly overall correlated with NPCJP9ACHigh correlation
NPCJP9AG is highly overall correlated with NPCJP9AHHigh correlation
NPCJP9AH is highly overall correlated with NPCJP10 and 2 other fieldsHigh correlation
NPCJP9AJ is highly overall correlated with NPCJP9AHHigh correlation
NPCJP1B has 259072 (99.5%) missing valuesMissing
NPCJP1C has 257735 (98.9%) missing valuesMissing
NPCJP1D has 260014 (99.8%) missing valuesMissing
NPCJP1E has 260057 (99.8%) missing valuesMissing
NPCJP1F has 260066 (99.8%) missing valuesMissing
NPCJP1G has 259997 (99.8%) missing valuesMissing
NPCJP1H has 259894 (99.8%) missing valuesMissing
NPCJP1I has 258663 (99.3%) missing valuesMissing
NPCJP1J has 259443 (99.6%) missing valuesMissing
NPCJP1K has 260287 (99.9%) missing valuesMissing
NPCJP1L has 260075 (99.8%) missing valuesMissing
NPCJP1M has 259874 (99.8%) missing valuesMissing
NPCJP1N has 260240 (99.9%) missing valuesMissing
NPCJP1A has 247098 (94.9%) missing valuesMissing
NPCJP1O has 260414 (> 99.9%) missing valuesMissing
NPCJP1P has 259273 (99.5%) missing valuesMissing
NPCJP1Q has 23802 (9.1%) missing valuesMissing
NPCJP2 has 236674 (90.9%) missing valuesMissing
NPCJP3 has 244901 (94.0%) missing valuesMissing
NPCJP6 has 244901 (94.0%) missing valuesMissing
NPCJP7 has 23802 (9.1%) missing valuesMissing
NPCJP9A has 124214 (47.7%) missing valuesMissing
NPCJP9B has 222974 (85.6%) missing valuesMissing
NPCJP9C has 99587 (38.2%) missing valuesMissing
NPCJP9D has 252334 (96.9%) missing valuesMissing
NPCJP9E has 244703 (93.9%) missing valuesMissing
NPCJP9F has 259238 (99.5%) missing valuesMissing
NPCJP9G has 259286 (99.5%) missing valuesMissing
NPCJP9H has 259995 (99.8%) missing valuesMissing
NPCJP9I has 257472 (98.8%) missing valuesMissing
NPCJP9J has 243797 (93.6%) missing valuesMissing
NPCJP8A has 65164 (25.0%) missing valuesMissing
NPCJP8B has 157534 (60.5%) missing valuesMissing
NPCJP8C has 221226 (84.9%) missing valuesMissing
NPCJP8D has 255406 (98.1%) missing valuesMissing
NPCJP8E has 257577 (98.9%) missing valuesMissing
NPCJP8F has 257740 (98.9%) missing valuesMissing
NPCJP8G has 259313 (99.6%) missing valuesMissing
NPCJP8H has 237757 (91.3%) missing valuesMissing
NPCJP9AA has 19096 (7.3%) missing valuesMissing
NPCJP9AB has 19096 (7.3%) missing valuesMissing
NPCJP9AC has 19096 (7.3%) missing valuesMissing
NPCJP9AD has 19096 (7.3%) missing valuesMissing
NPCJP9AE has 19096 (7.3%) missing valuesMissing
NPCJP9AF has 19096 (7.3%) missing valuesMissing
NPCJP9AG has 19096 (7.3%) missing valuesMissing
NPCJP9AH has 19096 (7.3%) missing valuesMissing
NPCJP9AI has 19096 (7.3%) missing valuesMissing
NPCJP9AJ has 19096 (7.3%) missing valuesMissing
NPCJP9AK has 19096 (7.3%) missing valuesMissing
NPCJP10 has 19096 (7.3%) missing valuesMissing
NPCJP11 has 19096 (7.3%) missing valuesMissing
NPCJP12 has 19096 (7.3%) missing valuesMissing
NPCJP13 has 19096 (7.3%) missing valuesMissing
SECUENCIA_P is highly skewed (γ1 = 20.48833828)Skewed
DIRECTORIO_PER has unique valuesUnique
NPCJP9AC has 32272 (12.4%) zerosZeros
NPCJP9AE has 45132 (17.3%) zerosZeros
NPCJP9AF has 5294 (2.0%) zerosZeros
NPCJP9AG has 5564 (2.1%) zerosZeros
NPCJP9AI has 2724 (1.0%) zerosZeros
NPCJP11 has 69723 (26.8%) zerosZeros
NPCJP12 has 107643 (41.3%) zerosZeros

Reproduction

Analysis started2024-04-22 02:15:42.836965
Analysis finished2024-04-22 02:17:19.417037
Duration1 minute and 36.58 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

DIRECTORIO
Real number (ℝ)

HIGH CORRELATION 

Distinct106467
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1116030.6
Minimum166238
Maximum3006812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:19.567007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum166238
5-th percentile330670
Q1997914
median1052432
Q31151021.2
95-th percentile3000854.2
Maximum3006812
Range2840574
Interquartile range (IQR)153107.25

Descriptive statistics

Standard deviation605289.29
Coefficient of variation (CV)0.54235905
Kurtosis3.2207147
Mean1116030.6
Median Absolute Deviation (MAD)85257.5
Skewness1.5690728
Sum2.9069919 × 1011
Variance3.6637513 × 1011
MonotonicityIncreasing
2024-04-21T21:17:19.802799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990174 20
 
< 0.1%
1166213 20
 
< 0.1%
342432 15
 
< 0.1%
1165204 14
 
< 0.1%
326730 13
 
< 0.1%
1024026 12
 
< 0.1%
998147 11
 
< 0.1%
1157479 11
 
< 0.1%
327474 11
 
< 0.1%
1016627 11
 
< 0.1%
Other values (106457) 260338
99.9%
ValueCountFrequency (%)
166238 2
 
< 0.1%
220102 4
< 0.1%
220385 3
< 0.1%
222175 5
< 0.1%
227359 2
 
< 0.1%
227362 2
 
< 0.1%
229477 4
< 0.1%
229508 4
< 0.1%
229753 2
 
< 0.1%
233197 4
< 0.1%
ValueCountFrequency (%)
3006812 3
< 0.1%
3006811 3
< 0.1%
3006810 5
< 0.1%
3006809 1
 
< 0.1%
3006808 2
 
< 0.1%
3006807 2
 
< 0.1%
3006806 3
< 0.1%
3006805 2
 
< 0.1%
3006804 2
 
< 0.1%
3006803 3
< 0.1%

DIRECTORIO_HOG
Real number (ℝ)

HIGH CORRELATION 

Distinct107119
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11160307
Minimum1662381
Maximum30068121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:20.040709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1662381
5-th percentile3306701
Q19979141
median10524321
Q311510214
95-th percentile30008544
Maximum30068121
Range28405740
Interquartile range (IQR)1531072.5

Descriptive statistics

Standard deviation6052892.9
Coefficient of variation (CV)0.542359
Kurtosis3.2207147
Mean11160307
Median Absolute Deviation (MAD)852575
Skewness1.5690728
Sum2.9069921 × 1012
Variance3.6637513 × 1013
MonotonicityIncreasing
2024-04-21T21:17:20.275692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11652041 14
 
< 0.1%
3267301 13
 
< 0.1%
10240261 12
 
< 0.1%
21931501 11
 
< 0.1%
9981471 11
 
< 0.1%
11024611 10
 
< 0.1%
11385171 10
 
< 0.1%
10441791 10
 
< 0.1%
11467901 10
 
< 0.1%
11533751 10
 
< 0.1%
Other values (107109) 260365
> 99.9%
ValueCountFrequency (%)
1662381 2
 
< 0.1%
2201021 4
< 0.1%
2203851 3
< 0.1%
2221751 5
< 0.1%
2273591 2
 
< 0.1%
2273621 2
 
< 0.1%
2294771 4
< 0.1%
2295081 4
< 0.1%
2297531 2
 
< 0.1%
2331971 4
< 0.1%
ValueCountFrequency (%)
30068121 3
< 0.1%
30068111 3
< 0.1%
30068101 5
< 0.1%
30068091 1
 
< 0.1%
30068081 2
 
< 0.1%
30068071 2
 
< 0.1%
30068061 3
< 0.1%
30068051 2
 
< 0.1%
30068041 2
 
< 0.1%
30068031 3
< 0.1%

DIRECTORIO_PER
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct260476
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1182445 × 108
Minimum16623811
Maximum2.1931501 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:20.522149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum16623811
5-th percentile33067312
Q199792413
median1.0524501 × 108
Q31.1510561 × 108
95-th percentile3.0008871 × 108
Maximum2.1931501 × 109
Range2.1765263 × 109
Interquartile range (IQR)15313198

Descriptive statistics

Standard deviation62411333
Coefficient of variation (CV)0.55811885
Kurtosis29.783609
Mean1.1182445 × 108
Median Absolute Deviation (MAD)8525201
Skewness2.607861
Sum2.9127585 × 1013
Variance3.8951745 × 1015
MonotonicityNot monotonic
2024-04-21T21:17:20.767129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16623811 1
 
< 0.1%
112867112 1
 
< 0.1%
112865812 1
 
< 0.1%
112865813 1
 
< 0.1%
112866111 1
 
< 0.1%
112866211 1
 
< 0.1%
112866212 1
 
< 0.1%
112866411 1
 
< 0.1%
112866412 1
 
< 0.1%
112866811 1
 
< 0.1%
Other values (260466) 260466
> 99.9%
ValueCountFrequency (%)
16623811 1
< 0.1%
16623812 1
< 0.1%
22010211 1
< 0.1%
22010212 1
< 0.1%
22010213 1
< 0.1%
22010214 1
< 0.1%
22038511 1
< 0.1%
22038512 1
< 0.1%
22038513 1
< 0.1%
22217511 1
< 0.1%
ValueCountFrequency (%)
2193150111 1
< 0.1%
2193150110 1
< 0.1%
2187710110 1
< 0.1%
1930487110 1
< 0.1%
1165204114 1
< 0.1%
1165204113 1
< 0.1%
1165204112 1
< 0.1%
1165204111 1
< 0.1%
1165204110 1
< 0.1%
1155618111 1
< 0.1%

SECUENCIA_P
Real number (ℝ)

SKEWED 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0062079
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:20.959574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09648714
Coefficient of variation (CV)0.095891857
Kurtosis551.91597
Mean1.0062079
Median Absolute Deviation (MAD)0
Skewness20.488338
Sum262093
Variance0.0093097682
MonotonicityNot monotonic
2024-04-21T21:17:21.133125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 259166
99.5%
2 1082
 
0.4%
3 169
 
0.1%
4 42
 
< 0.1%
5 14
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
1 259166
99.5%
2 1082
 
0.4%
3 169
 
0.1%
4 42
 
< 0.1%
5 14
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 14
 
< 0.1%
4 42
 
< 0.1%
3 169
 
0.1%
2 1082
 
0.4%
1 259166
99.5%

ORDEN
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0180708
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:21.302442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1461004
Coefficient of variation (CV)0.56791885
Kurtosis2.9034924
Mean2.0180708
Median Absolute Deviation (MAD)1
Skewness1.3989394
Sum525659
Variance1.3135462
MonotonicityNot monotonic
2024-04-21T21:17:21.490829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 107119
41.1%
2 82667
31.7%
3 42931
16.5%
4 18851
 
7.2%
5 6036
 
2.3%
6 1901
 
0.7%
7 598
 
0.2%
8 233
 
0.1%
9 73
 
< 0.1%
10 39
 
< 0.1%
Other values (4) 28
 
< 0.1%
ValueCountFrequency (%)
1 107119
41.1%
2 82667
31.7%
3 42931
16.5%
4 18851
 
7.2%
5 6036
 
2.3%
6 1901
 
0.7%
7 598
 
0.2%
8 233
 
0.1%
9 73
 
< 0.1%
10 39
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 5
 
< 0.1%
12 5
 
< 0.1%
11 17
 
< 0.1%
10 39
 
< 0.1%
9 73
 
< 0.1%
8 233
 
0.1%
7 598
 
0.2%
6 1901
 
0.7%
5 6036
2.3%

NPCJP1B
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing259072
Missing (%)99.5%
Memory size2.0 MiB
1.0
1404 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4212
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1404
 
0.5%
(Missing) 259072
99.5%

Length

2024-04-21T21:17:21.690611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:21.860308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1404
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1404
33.3%
. 1404
33.3%
0 1404
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2808
66.7%
Other Punctuation 1404
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1404
50.0%
0 1404
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4212
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1404
33.3%
. 1404
33.3%
0 1404
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1404
33.3%
. 1404
33.3%
0 1404
33.3%

NPCJP1C
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing257735
Missing (%)98.9%
Memory size2.0 MiB
1.0
2741 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8223
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2741
 
1.1%
(Missing) 257735
98.9%

Length

2024-04-21T21:17:22.023105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:22.173613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2741
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2741
33.3%
. 2741
33.3%
0 2741
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5482
66.7%
Other Punctuation 2741
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2741
50.0%
0 2741
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2741
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8223
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2741
33.3%
. 2741
33.3%
0 2741
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2741
33.3%
. 2741
33.3%
0 2741
33.3%

NPCJP1D
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing260014
Missing (%)99.8%
Memory size2.0 MiB
1.0
462 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1386
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 462
 
0.2%
(Missing) 260014
99.8%

Length

2024-04-21T21:17:22.324487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:22.461947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 462
100.0%

Most occurring characters

ValueCountFrequency (%)
1 462
33.3%
. 462
33.3%
0 462
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 924
66.7%
Other Punctuation 462
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 462
50.0%
0 462
50.0%
Other Punctuation
ValueCountFrequency (%)
. 462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1386
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 462
33.3%
. 462
33.3%
0 462
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 462
33.3%
. 462
33.3%
0 462
33.3%

NPCJP1E
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing260057
Missing (%)99.8%
Memory size2.0 MiB
1.0
419 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1257
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 419
 
0.2%
(Missing) 260057
99.8%

Length

2024-04-21T21:17:22.609817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:22.767343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 419
100.0%

Most occurring characters

ValueCountFrequency (%)
1 419
33.3%
. 419
33.3%
0 419
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 838
66.7%
Other Punctuation 419
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 419
50.0%
0 419
50.0%
Other Punctuation
ValueCountFrequency (%)
. 419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1257
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 419
33.3%
. 419
33.3%
0 419
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 419
33.3%
. 419
33.3%
0 419
33.3%

NPCJP1F
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing260066
Missing (%)99.8%
Memory size2.0 MiB
1.0
410 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1230
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 410
 
0.2%
(Missing) 260066
99.8%

Length

2024-04-21T21:17:22.943164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:23.101806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 410
100.0%

Most occurring characters

ValueCountFrequency (%)
1 410
33.3%
. 410
33.3%
0 410
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 820
66.7%
Other Punctuation 410
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 410
50.0%
0 410
50.0%
Other Punctuation
ValueCountFrequency (%)
. 410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1230
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 410
33.3%
. 410
33.3%
0 410
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 410
33.3%
. 410
33.3%
0 410
33.3%

NPCJP1G
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing259997
Missing (%)99.8%
Memory size2.0 MiB
1.0
479 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1437
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 479
 
0.2%
(Missing) 259997
99.8%

Length

2024-04-21T21:17:23.268261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:23.411856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 479
100.0%

Most occurring characters

ValueCountFrequency (%)
1 479
33.3%
. 479
33.3%
0 479
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 958
66.7%
Other Punctuation 479
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 479
50.0%
0 479
50.0%
Other Punctuation
ValueCountFrequency (%)
. 479
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1437
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 479
33.3%
. 479
33.3%
0 479
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 479
33.3%
. 479
33.3%
0 479
33.3%

NPCJP1H
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing259894
Missing (%)99.8%
Memory size2.0 MiB
1.0
582 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1746
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 582
 
0.2%
(Missing) 259894
99.8%

Length

2024-04-21T21:17:23.568131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:23.722342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 582
100.0%

Most occurring characters

ValueCountFrequency (%)
1 582
33.3%
. 582
33.3%
0 582
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1164
66.7%
Other Punctuation 582
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 582
50.0%
0 582
50.0%
Other Punctuation
ValueCountFrequency (%)
. 582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1746
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 582
33.3%
. 582
33.3%
0 582
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 582
33.3%
. 582
33.3%
0 582
33.3%

NPCJP1I
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing258663
Missing (%)99.3%
Memory size2.0 MiB
1.0
1813 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5439
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1813
 
0.7%
(Missing) 258663
99.3%

Length

2024-04-21T21:17:23.885427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:24.051976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1813
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1813
33.3%
. 1813
33.3%
0 1813
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3626
66.7%
Other Punctuation 1813
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1813
50.0%
0 1813
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5439
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1813
33.3%
. 1813
33.3%
0 1813
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1813
33.3%
. 1813
33.3%
0 1813
33.3%

NPCJP1J
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing259443
Missing (%)99.6%
Memory size2.0 MiB
1.0
1033 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3099
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1033
 
0.4%
(Missing) 259443
99.6%

Length

2024-04-21T21:17:24.215899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:24.366543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1033
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1033
33.3%
. 1033
33.3%
0 1033
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2066
66.7%
Other Punctuation 1033
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1033
50.0%
0 1033
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1033
33.3%
. 1033
33.3%
0 1033
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1033
33.3%
. 1033
33.3%
0 1033
33.3%

NPCJP1K
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing260287
Missing (%)99.9%
Memory size2.0 MiB
1.0
189 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters567
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 189
 
0.1%
(Missing) 260287
99.9%

Length

2024-04-21T21:17:24.526955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:24.698578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 189
100.0%

Most occurring characters

ValueCountFrequency (%)
1 189
33.3%
. 189
33.3%
0 189
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 378
66.7%
Other Punctuation 189
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 189
50.0%
0 189
50.0%
Other Punctuation
ValueCountFrequency (%)
. 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 567
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 189
33.3%
. 189
33.3%
0 189
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 189
33.3%
. 189
33.3%
0 189
33.3%

NPCJP1L
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing260075
Missing (%)99.8%
Memory size2.0 MiB
1.0
401 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1203
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 401
 
0.2%
(Missing) 260075
99.8%

Length

2024-04-21T21:17:24.870647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:25.030021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 401
100.0%

Most occurring characters

ValueCountFrequency (%)
1 401
33.3%
. 401
33.3%
0 401
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 802
66.7%
Other Punctuation 401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 401
50.0%
0 401
50.0%
Other Punctuation
ValueCountFrequency (%)
. 401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 401
33.3%
. 401
33.3%
0 401
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 401
33.3%
. 401
33.3%
0 401
33.3%

NPCJP1M
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing259874
Missing (%)99.8%
Memory size2.0 MiB
1.0
602 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1806
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 602
 
0.2%
(Missing) 259874
99.8%

Length

2024-04-21T21:17:25.212955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:25.394738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 602
100.0%

Most occurring characters

ValueCountFrequency (%)
1 602
33.3%
. 602
33.3%
0 602
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1204
66.7%
Other Punctuation 602
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 602
50.0%
0 602
50.0%
Other Punctuation
ValueCountFrequency (%)
. 602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 602
33.3%
. 602
33.3%
0 602
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 602
33.3%
. 602
33.3%
0 602
33.3%

NPCJP1N
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing260240
Missing (%)99.9%
Memory size2.0 MiB
1.0
236 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters708
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 236
 
0.1%
(Missing) 260240
99.9%

Length

2024-04-21T21:17:25.550239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:25.695340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 236
100.0%

Most occurring characters

ValueCountFrequency (%)
1 236
33.3%
. 236
33.3%
0 236
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 472
66.7%
Other Punctuation 236
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 236
50.0%
0 236
50.0%
Other Punctuation
ValueCountFrequency (%)
. 236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 708
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 236
33.3%
. 236
33.3%
0 236
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 236
33.3%
. 236
33.3%
0 236
33.3%

NPCJP1A
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing247098
Missing (%)94.9%
Memory size2.0 MiB
1.0
13378 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters40134
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 13378
 
5.1%
(Missing) 247098
94.9%

Length

2024-04-21T21:17:25.857076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:26.014018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 13378
100.0%

Most occurring characters

ValueCountFrequency (%)
1 13378
33.3%
. 13378
33.3%
0 13378
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26756
66.7%
Other Punctuation 13378
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13378
50.0%
0 13378
50.0%
Other Punctuation
ValueCountFrequency (%)
. 13378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40134
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13378
33.3%
. 13378
33.3%
0 13378
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13378
33.3%
. 13378
33.3%
0 13378
33.3%

NPCJP1O
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.6%
Missing260414
Missing (%)> 99.9%
Memory size2.0 MiB
1.0
62 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters186
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 62
 
< 0.1%
(Missing) 260414
> 99.9%

Length

2024-04-21T21:17:26.179226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:26.330990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 62
100.0%

Most occurring characters

ValueCountFrequency (%)
1 62
33.3%
. 62
33.3%
0 62
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124
66.7%
Other Punctuation 62
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 62
50.0%
0 62
50.0%
Other Punctuation
ValueCountFrequency (%)
. 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 62
33.3%
. 62
33.3%
0 62
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 62
33.3%
. 62
33.3%
0 62
33.3%

NPCJP1P
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing259273
Missing (%)99.5%
Memory size2.0 MiB
1.0
1203 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3609
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1203
 
0.5%
(Missing) 259273
99.5%

Length

2024-04-21T21:17:26.502422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:26.660874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1203
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1203
33.3%
. 1203
33.3%
0 1203
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2406
66.7%
Other Punctuation 1203
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1203
50.0%
0 1203
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1203
33.3%
. 1203
33.3%
0 1203
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1203
33.3%
. 1203
33.3%
0 1203
33.3%

NPCJP1Q
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing23802
Missing (%)9.1%
Memory size2.0 MiB
1.0
236674 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters710022
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 236674
90.9%
(Missing) 23802
 
9.1%

Length

2024-04-21T21:17:26.840358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:27.003797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 236674
100.0%

Most occurring characters

ValueCountFrequency (%)
1 236674
33.3%
. 236674
33.3%
0 236674
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 473348
66.7%
Other Punctuation 236674
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 236674
50.0%
0 236674
50.0%
Other Punctuation
ValueCountFrequency (%)
. 236674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 710022
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 236674
33.3%
. 236674
33.3%
0 236674
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 710022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 236674
33.3%
. 236674
33.3%
0 236674
33.3%

NPCJP2
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing236674
Missing (%)90.9%
Memory size2.0 MiB
1.0
15575 
2.0
8227 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters71406
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15575
 
6.0%
2.0 8227
 
3.2%
(Missing) 236674
90.9%

Length

2024-04-21T21:17:27.160455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:27.326700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15575
65.4%
2.0 8227
34.6%

Most occurring characters

ValueCountFrequency (%)
. 23802
33.3%
0 23802
33.3%
1 15575
21.8%
2 8227
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47604
66.7%
Other Punctuation 23802
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23802
50.0%
1 15575
32.7%
2 8227
 
17.3%
Other Punctuation
ValueCountFrequency (%)
. 23802
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23802
33.3%
0 23802
33.3%
1 15575
21.8%
2 8227
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23802
33.3%
0 23802
33.3%
1 15575
21.8%
2 8227
 
11.5%

NPCJP3
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing244901
Missing (%)94.0%
Memory size2.0 MiB
2.0
10197 
1.0
5378 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters46725
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 10197
 
3.9%
1.0 5378
 
2.1%
(Missing) 244901
94.0%

Length

2024-04-21T21:17:27.498557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:27.663851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 10197
65.5%
1.0 5378
34.5%

Most occurring characters

ValueCountFrequency (%)
. 15575
33.3%
0 15575
33.3%
2 10197
21.8%
1 5378
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31150
66.7%
Other Punctuation 15575
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15575
50.0%
2 10197
32.7%
1 5378
 
17.3%
Other Punctuation
ValueCountFrequency (%)
. 15575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46725
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15575
33.3%
0 15575
33.3%
2 10197
21.8%
1 5378
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15575
33.3%
0 15575
33.3%
2 10197
21.8%
1 5378
 
11.5%

NPCJP6
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing244901
Missing (%)94.0%
Memory size2.0 MiB
4.0
6315 
2.0
4072 
3.0
3875 
9.0
796 
1.0
 
517

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters46725
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0 6315
 
2.4%
2.0 4072
 
1.6%
3.0 3875
 
1.5%
9.0 796
 
0.3%
1.0 517
 
0.2%
(Missing) 244901
94.0%

Length

2024-04-21T21:17:27.838803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:28.017507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 6315
40.5%
2.0 4072
26.1%
3.0 3875
24.9%
9.0 796
 
5.1%
1.0 517
 
3.3%

Most occurring characters

ValueCountFrequency (%)
. 15575
33.3%
0 15575
33.3%
4 6315
13.5%
2 4072
 
8.7%
3 3875
 
8.3%
9 796
 
1.7%
1 517
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31150
66.7%
Other Punctuation 15575
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15575
50.0%
4 6315
20.3%
2 4072
 
13.1%
3 3875
 
12.4%
9 796
 
2.6%
1 517
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 15575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46725
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15575
33.3%
0 15575
33.3%
4 6315
13.5%
2 4072
 
8.7%
3 3875
 
8.3%
9 796
 
1.7%
1 517
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15575
33.3%
0 15575
33.3%
4 6315
13.5%
2 4072
 
8.7%
3 3875
 
8.3%
9 796
 
1.7%
1 517
 
1.1%

NPCJP7
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing23802
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean5.0531153
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:28.190602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3009304
Coefficient of variation (CV)0.45534888
Kurtosis-0.5164247
Mean5.0531153
Median Absolute Deviation (MAD)2
Skewness-0.170166
Sum1195941
Variance5.2942807
MonotonicityNot monotonic
2024-04-21T21:17:28.374407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 98369
37.8%
7 41321
15.9%
1 31102
 
11.9%
9 26019
 
10.0%
3 18506
 
7.1%
6 7834
 
3.0%
2 7763
 
3.0%
4 3638
 
1.4%
8 2122
 
0.8%
(Missing) 23802
 
9.1%
ValueCountFrequency (%)
1 31102
 
11.9%
2 7763
 
3.0%
3 18506
 
7.1%
4 3638
 
1.4%
5 98369
37.8%
6 7834
 
3.0%
7 41321
15.9%
8 2122
 
0.8%
9 26019
 
10.0%
ValueCountFrequency (%)
9 26019
 
10.0%
8 2122
 
0.8%
7 41321
15.9%
6 7834
 
3.0%
5 98369
37.8%
4 3638
 
1.4%
3 18506
 
7.1%
2 7763
 
3.0%
1 31102
 
11.9%

NPCJP9A
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing124214
Missing (%)47.7%
Memory size2.0 MiB
1.0
136262 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters408786
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 136262
52.3%
(Missing) 124214
47.7%

Length

2024-04-21T21:17:28.581737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:28.737340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 136262
100.0%

Most occurring characters

ValueCountFrequency (%)
1 136262
33.3%
. 136262
33.3%
0 136262
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 272524
66.7%
Other Punctuation 136262
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 136262
50.0%
0 136262
50.0%
Other Punctuation
ValueCountFrequency (%)
. 136262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 408786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 136262
33.3%
. 136262
33.3%
0 136262
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 408786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 136262
33.3%
. 136262
33.3%
0 136262
33.3%

NPCJP9B
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing222974
Missing (%)85.6%
Memory size2.0 MiB
1.0
37502 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters112506
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 37502
 
14.4%
(Missing) 222974
85.6%

Length

2024-04-21T21:17:28.901886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:29.060151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 37502
100.0%

Most occurring characters

ValueCountFrequency (%)
1 37502
33.3%
. 37502
33.3%
0 37502
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75004
66.7%
Other Punctuation 37502
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37502
50.0%
0 37502
50.0%
Other Punctuation
ValueCountFrequency (%)
. 37502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37502
33.3%
. 37502
33.3%
0 37502
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37502
33.3%
. 37502
33.3%
0 37502
33.3%

NPCJP9C
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing99587
Missing (%)38.2%
Memory size2.0 MiB
1.0
160889 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters482667
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 160889
61.8%
(Missing) 99587
38.2%

Length

2024-04-21T21:17:29.227941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:29.388825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 160889
100.0%

Most occurring characters

ValueCountFrequency (%)
1 160889
33.3%
. 160889
33.3%
0 160889
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321778
66.7%
Other Punctuation 160889
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 160889
50.0%
0 160889
50.0%
Other Punctuation
ValueCountFrequency (%)
. 160889
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 482667
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 160889
33.3%
. 160889
33.3%
0 160889
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 482667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 160889
33.3%
. 160889
33.3%
0 160889
33.3%

NPCJP9D
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing252334
Missing (%)96.9%
Memory size2.0 MiB
1.0
8142 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24426
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8142
 
3.1%
(Missing) 252334
96.9%

Length

2024-04-21T21:17:29.551263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:29.708213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8142
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8142
33.3%
. 8142
33.3%
0 8142
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16284
66.7%
Other Punctuation 8142
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8142
50.0%
0 8142
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24426
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8142
33.3%
. 8142
33.3%
0 8142
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8142
33.3%
. 8142
33.3%
0 8142
33.3%

NPCJP9E
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing244703
Missing (%)93.9%
Memory size2.0 MiB
1.0
15773 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47319
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15773
 
6.1%
(Missing) 244703
93.9%

Length

2024-04-21T21:17:29.876905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:30.035082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15773
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15773
33.3%
. 15773
33.3%
0 15773
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31546
66.7%
Other Punctuation 15773
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15773
50.0%
0 15773
50.0%
Other Punctuation
ValueCountFrequency (%)
. 15773
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47319
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15773
33.3%
. 15773
33.3%
0 15773
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15773
33.3%
. 15773
33.3%
0 15773
33.3%

NPCJP9F
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing259238
Missing (%)99.5%
Memory size2.0 MiB
1.0
1238 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3714
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1238
 
0.5%
(Missing) 259238
99.5%

Length

2024-04-21T21:17:30.199231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:30.348792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1238
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1238
33.3%
. 1238
33.3%
0 1238
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2476
66.7%
Other Punctuation 1238
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1238
50.0%
0 1238
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1238
33.3%
. 1238
33.3%
0 1238
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1238
33.3%
. 1238
33.3%
0 1238
33.3%

NPCJP9G
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing259286
Missing (%)99.5%
Memory size2.0 MiB
1.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1190
 
0.5%
(Missing) 259286
99.5%

Length

2024-04-21T21:17:30.506393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:30.650980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1190
33.3%
. 1190
33.3%
0 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1190
50.0%
0 1190
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1190
33.3%
. 1190
33.3%
0 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1190
33.3%
. 1190
33.3%
0 1190
33.3%

NPCJP9H
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing259995
Missing (%)99.8%
Memory size2.0 MiB
1.0
481 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1443
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 481
 
0.2%
(Missing) 259995
99.8%

Length

2024-04-21T21:17:30.804626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:30.944537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 481
100.0%

Most occurring characters

ValueCountFrequency (%)
1 481
33.3%
. 481
33.3%
0 481
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 962
66.7%
Other Punctuation 481
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 481
50.0%
0 481
50.0%
Other Punctuation
ValueCountFrequency (%)
. 481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1443
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 481
33.3%
. 481
33.3%
0 481
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 481
33.3%
. 481
33.3%
0 481
33.3%

NPCJP9I
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing257472
Missing (%)98.8%
Memory size2.0 MiB
1.0
3004 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9012
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3004
 
1.2%
(Missing) 257472
98.8%

Length

2024-04-21T21:17:31.097154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:31.249844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3004
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3004
33.3%
. 3004
33.3%
0 3004
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6008
66.7%
Other Punctuation 3004
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3004
50.0%
0 3004
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3004
33.3%
. 3004
33.3%
0 3004
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3004
33.3%
. 3004
33.3%
0 3004
33.3%

NPCJP9J
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing243797
Missing (%)93.6%
Memory size2.0 MiB
1.0
16679 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters50037
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 16679
 
6.4%
(Missing) 243797
93.6%

Length

2024-04-21T21:17:31.413239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:31.574008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 16679
100.0%

Most occurring characters

ValueCountFrequency (%)
1 16679
33.3%
. 16679
33.3%
0 16679
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33358
66.7%
Other Punctuation 16679
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16679
50.0%
0 16679
50.0%
Other Punctuation
ValueCountFrequency (%)
. 16679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16679
33.3%
. 16679
33.3%
0 16679
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16679
33.3%
. 16679
33.3%
0 16679
33.3%

NPCJP8A
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing65164
Missing (%)25.0%
Memory size2.0 MiB
1.0
195312 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters585936
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 195312
75.0%
(Missing) 65164
 
25.0%

Length

2024-04-21T21:17:31.735490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:31.886460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 195312
100.0%

Most occurring characters

ValueCountFrequency (%)
1 195312
33.3%
. 195312
33.3%
0 195312
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390624
66.7%
Other Punctuation 195312
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 195312
50.0%
0 195312
50.0%
Other Punctuation
ValueCountFrequency (%)
. 195312
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 585936
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 195312
33.3%
. 195312
33.3%
0 195312
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 585936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 195312
33.3%
. 195312
33.3%
0 195312
33.3%

NPCJP8B
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing157534
Missing (%)60.5%
Memory size2.0 MiB
1.0
102942 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters308826
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 102942
39.5%
(Missing) 157534
60.5%

Length

2024-04-21T21:17:32.047510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:32.185538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 102942
100.0%

Most occurring characters

ValueCountFrequency (%)
1 102942
33.3%
. 102942
33.3%
0 102942
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 205884
66.7%
Other Punctuation 102942
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 102942
50.0%
0 102942
50.0%
Other Punctuation
ValueCountFrequency (%)
. 102942
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308826
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 102942
33.3%
. 102942
33.3%
0 102942
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 102942
33.3%
. 102942
33.3%
0 102942
33.3%

NPCJP8C
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing221226
Missing (%)84.9%
Memory size2.0 MiB
1.0
39250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters117750
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 39250
 
15.1%
(Missing) 221226
84.9%

Length

2024-04-21T21:17:32.332097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:32.477758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 39250
100.0%

Most occurring characters

ValueCountFrequency (%)
1 39250
33.3%
. 39250
33.3%
0 39250
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78500
66.7%
Other Punctuation 39250
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39250
50.0%
0 39250
50.0%
Other Punctuation
ValueCountFrequency (%)
. 39250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 39250
33.3%
. 39250
33.3%
0 39250
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 39250
33.3%
. 39250
33.3%
0 39250
33.3%

NPCJP8D
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing255406
Missing (%)98.1%
Memory size2.0 MiB
1.0
5070 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15210
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5070
 
1.9%
(Missing) 255406
98.1%

Length

2024-04-21T21:17:32.630272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:32.769705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5070
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5070
33.3%
. 5070
33.3%
0 5070
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10140
66.7%
Other Punctuation 5070
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5070
50.0%
0 5070
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5070
33.3%
. 5070
33.3%
0 5070
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5070
33.3%
. 5070
33.3%
0 5070
33.3%

NPCJP8E
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing257577
Missing (%)98.9%
Memory size2.0 MiB
1.0
2899 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8697
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2899
 
1.1%
(Missing) 257577
98.9%

Length

2024-04-21T21:17:32.920200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:33.060069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2899
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2899
33.3%
. 2899
33.3%
0 2899
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5798
66.7%
Other Punctuation 2899
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2899
50.0%
0 2899
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2899
33.3%
. 2899
33.3%
0 2899
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2899
33.3%
. 2899
33.3%
0 2899
33.3%

NPCJP8F
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing257740
Missing (%)98.9%
Memory size2.0 MiB
1.0
2736 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8208
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2736
 
1.1%
(Missing) 257740
98.9%

Length

2024-04-21T21:17:33.205557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:33.344995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2736
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2736
33.3%
. 2736
33.3%
0 2736
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5472
66.7%
Other Punctuation 2736
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2736
50.0%
0 2736
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8208
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2736
33.3%
. 2736
33.3%
0 2736
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2736
33.3%
. 2736
33.3%
0 2736
33.3%

NPCJP8G
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing259313
Missing (%)99.6%
Memory size2.0 MiB
1.0
1163 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3489
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1163
 
0.4%
(Missing) 259313
99.6%

Length

2024-04-21T21:17:33.496800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:33.638387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1163
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1163
33.3%
. 1163
33.3%
0 1163
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2326
66.7%
Other Punctuation 1163
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1163
50.0%
0 1163
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3489
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1163
33.3%
. 1163
33.3%
0 1163
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1163
33.3%
. 1163
33.3%
0 1163
33.3%

NPCJP8H
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing237757
Missing (%)91.3%
Memory size2.0 MiB
1.0
22719 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters68157
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 22719
 
8.7%
(Missing) 237757
91.3%

Length

2024-04-21T21:17:33.786239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:17:33.916442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 22719
100.0%

Most occurring characters

ValueCountFrequency (%)
1 22719
33.3%
. 22719
33.3%
0 22719
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45438
66.7%
Other Punctuation 22719
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22719
50.0%
0 22719
50.0%
Other Punctuation
ValueCountFrequency (%)
. 22719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22719
33.3%
. 22719
33.3%
0 22719
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22719
33.3%
. 22719
33.3%
0 22719
33.3%

NPCJP9AA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean8.3854586
Minimum0
Maximum10
Zeros618
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:34.045651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median8
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.489403
Coefficient of variation (CV)0.17761735
Kurtosis3.9632005
Mean8.3854586
Median Absolute Deviation (MAD)1
Skewness-1.4528899
Sum2024082
Variance2.2183212
MonotonicityNot monotonic
2024-04-21T21:17:34.215922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 71770
27.6%
10 63507
24.4%
9 57130
21.9%
7 28117
 
10.8%
6 9899
 
3.8%
5 6387
 
2.5%
4 1819
 
0.7%
3 1209
 
0.5%
2 707
 
0.3%
0 618
 
0.2%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 618
 
0.2%
1 217
 
0.1%
2 707
 
0.3%
3 1209
 
0.5%
4 1819
 
0.7%
5 6387
 
2.5%
6 9899
 
3.8%
7 28117
 
10.8%
8 71770
27.6%
9 57130
21.9%
ValueCountFrequency (%)
10 63507
24.4%
9 57130
21.9%
8 71770
27.6%
7 28117
 
10.8%
6 9899
 
3.8%
5 6387
 
2.5%
4 1819
 
0.7%
3 1209
 
0.5%
2 707
 
0.3%
1 217
 
0.1%

NPCJP9AB
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean8.117429
Minimum0
Maximum10
Zeros1301
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:34.380777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7375833
Coefficient of variation (CV)0.21405587
Kurtosis3.0340867
Mean8.117429
Median Absolute Deviation (MAD)1
Skewness-1.4015586
Sum1959385
Variance3.0191959
MonotonicityNot monotonic
2024-04-21T21:17:34.551698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 67552
25.9%
10 58689
22.5%
9 49548
19.0%
7 32044
12.3%
6 14455
 
5.5%
5 10102
 
3.9%
4 3720
 
1.4%
3 2249
 
0.9%
0 1301
 
0.5%
2 1252
 
0.5%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 1301
 
0.5%
1 468
 
0.2%
2 1252
 
0.5%
3 2249
 
0.9%
4 3720
 
1.4%
5 10102
 
3.9%
6 14455
 
5.5%
7 32044
12.3%
8 67552
25.9%
9 49548
19.0%
ValueCountFrequency (%)
10 58689
22.5%
9 49548
19.0%
8 67552
25.9%
7 32044
12.3%
6 14455
 
5.5%
5 10102
 
3.9%
4 3720
 
1.4%
3 2249
 
0.9%
2 1252
 
0.5%
1 468
 
0.2%

NPCJP9AC
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean5.9629837
Minimum0
Maximum10
Zeros32272
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:34.715629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1722163
Coefficient of variation (CV)0.53198473
Kurtosis-0.68303328
Mean5.9629837
Median Absolute Deviation (MAD)2
Skewness-0.71722189
Sum1439345
Variance10.062956
MonotonicityNot monotonic
2024-04-21T21:17:34.883400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 49152
18.9%
7 34477
13.2%
0 32272
12.4%
9 26819
10.3%
10 24402
9.4%
6 21544
8.3%
5 20413
7.8%
4 10116
 
3.9%
3 8962
 
3.4%
2 7497
 
2.9%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 32272
12.4%
1 5726
 
2.2%
2 7497
 
2.9%
3 8962
 
3.4%
4 10116
 
3.9%
5 20413
7.8%
6 21544
8.3%
7 34477
13.2%
8 49152
18.9%
9 26819
10.3%
ValueCountFrequency (%)
10 24402
9.4%
9 26819
10.3%
8 49152
18.9%
7 34477
13.2%
6 21544
8.3%
5 20413
7.8%
4 10116
 
3.9%
3 8962
 
3.4%
2 7497
 
2.9%
1 5726
 
2.2%

NPCJP9AD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean8.0793521
Minimum0
Maximum10
Zeros1329
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:35.043448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7581659
Coefficient of variation (CV)0.21761225
Kurtosis2.946487
Mean8.0793521
Median Absolute Deviation (MAD)1
Skewness-1.4035418
Sum1950194
Variance3.0911475
MonotonicityNot monotonic
2024-04-21T21:17:35.210039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 66449
25.5%
10 55850
21.4%
9 51748
19.9%
7 32416
12.4%
6 14814
 
5.7%
5 10388
 
4.0%
4 3925
 
1.5%
3 2525
 
1.0%
2 1423
 
0.5%
0 1329
 
0.5%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 1329
 
0.5%
1 513
 
0.2%
2 1423
 
0.5%
3 2525
 
1.0%
4 3925
 
1.5%
5 10388
 
4.0%
6 14814
 
5.7%
7 32416
12.4%
8 66449
25.5%
9 51748
19.9%
ValueCountFrequency (%)
10 55850
21.4%
9 51748
19.9%
8 66449
25.5%
7 32416
12.4%
6 14814
 
5.7%
5 10388
 
4.0%
4 3925
 
1.5%
3 2525
 
1.0%
2 1423
 
0.5%
1 513
 
0.2%

NPCJP9AE
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean5.8358605
Minimum0
Maximum10
Zeros45132
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:35.378406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.520045
Coefficient of variation (CV)0.60317497
Kurtosis-1.0712145
Mean5.8358605
Median Absolute Deviation (MAD)2
Skewness-0.62548853
Sum1408660
Variance12.390717
MonotonicityNot monotonic
2024-04-21T21:17:35.544797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 48504
18.6%
0 45132
17.3%
10 30827
11.8%
9 30687
11.8%
7 28573
11.0%
6 15908
 
6.1%
5 14210
 
5.5%
4 7192
 
2.8%
3 6956
 
2.7%
1 6752
 
2.6%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 45132
17.3%
1 6752
 
2.6%
2 6639
 
2.5%
3 6956
 
2.7%
4 7192
 
2.8%
5 14210
 
5.5%
6 15908
 
6.1%
7 28573
11.0%
8 48504
18.6%
9 30687
11.8%
ValueCountFrequency (%)
10 30827
11.8%
9 30687
11.8%
8 48504
18.6%
7 28573
11.0%
6 15908
 
6.1%
5 14210
 
5.5%
4 7192
 
2.8%
3 6956
 
2.7%
2 6639
 
2.5%
1 6752
 
2.6%

NPCJP9AF
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean6.7698857
Minimum0
Maximum10
Zeros5294
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:35.706576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2633813
Coefficient of variation (CV)0.33433081
Kurtosis0.60889052
Mean6.7698857
Median Absolute Deviation (MAD)1
Skewness-0.88230938
Sum1634115
Variance5.1228951
MonotonicityNot monotonic
2024-04-21T21:17:35.874039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 57589
22.1%
7 43606
16.7%
6 29370
11.3%
9 27359
10.5%
5 26059
10.0%
10 22460
 
8.6%
4 12446
 
4.8%
3 9116
 
3.5%
2 5602
 
2.2%
0 5294
 
2.0%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 5294
 
2.0%
1 2479
 
1.0%
2 5602
 
2.2%
3 9116
 
3.5%
4 12446
 
4.8%
5 26059
10.0%
6 29370
11.3%
7 43606
16.7%
8 57589
22.1%
9 27359
10.5%
ValueCountFrequency (%)
10 22460
 
8.6%
9 27359
10.5%
8 57589
22.1%
7 43606
16.7%
6 29370
11.3%
5 26059
10.0%
4 12446
 
4.8%
3 9116
 
3.5%
2 5602
 
2.2%
1 2479
 
1.0%

NPCJP9AG
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean7.6351852
Minimum0
Maximum10
Zeros5564
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:36.034791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.080615
Coefficient of variation (CV)0.27250355
Kurtosis3.1096512
Mean7.6351852
Median Absolute Deviation (MAD)1
Skewness-1.5609993
Sum1842981
Variance4.3289589
MonotonicityNot monotonic
2024-04-21T21:17:36.203726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 71514
27.5%
9 42794
16.4%
10 41100
15.8%
7 38663
14.8%
6 17585
 
6.8%
5 12601
 
4.8%
0 5564
 
2.1%
4 5074
 
1.9%
3 3306
 
1.3%
2 2174
 
0.8%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 5564
 
2.1%
1 1005
 
0.4%
2 2174
 
0.8%
3 3306
 
1.3%
4 5074
 
1.9%
5 12601
 
4.8%
6 17585
 
6.8%
7 38663
14.8%
8 71514
27.5%
9 42794
16.4%
ValueCountFrequency (%)
10 41100
15.8%
9 42794
16.4%
8 71514
27.5%
7 38663
14.8%
6 17585
 
6.8%
5 12601
 
4.8%
4 5074
 
1.9%
3 3306
 
1.3%
2 2174
 
0.8%
1 1005
 
0.4%

NPCJP9AH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean8.4923813
Minimum0
Maximum10
Zeros603
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:36.371979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median9
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5687407
Coefficient of variation (CV)0.1847233
Kurtosis3.5016593
Mean8.4923813
Median Absolute Deviation (MAD)1
Skewness-1.4919064
Sum2049891
Variance2.4609473
MonotonicityNot monotonic
2024-04-21T21:17:36.533056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 80155
30.8%
8 59301
22.8%
9 53111
20.4%
7 26470
 
10.2%
6 10523
 
4.0%
5 6396
 
2.5%
4 2388
 
0.9%
3 1350
 
0.5%
2 841
 
0.3%
0 603
 
0.2%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 603
 
0.2%
1 242
 
0.1%
2 841
 
0.3%
3 1350
 
0.5%
4 2388
 
0.9%
5 6396
 
2.5%
6 10523
 
4.0%
7 26470
10.2%
8 59301
22.8%
9 53111
20.4%
ValueCountFrequency (%)
10 80155
30.8%
9 53111
20.4%
8 59301
22.8%
7 26470
 
10.2%
6 10523
 
4.0%
5 6396
 
2.5%
4 2388
 
0.9%
3 1350
 
0.5%
2 841
 
0.3%
1 242
 
0.1%

NPCJP9AI
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean7.6349615
Minimum0
Maximum10
Zeros2724
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:36.684604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9869513
Coefficient of variation (CV)0.26024379
Kurtosis1.97391
Mean7.6349615
Median Absolute Deviation (MAD)1
Skewness-1.2115189
Sum1842927
Variance3.9479754
MonotonicityNot monotonic
2024-04-21T21:17:36.843641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 64527
24.8%
10 43990
16.9%
9 40266
15.5%
7 39286
15.1%
6 21380
 
8.2%
5 15607
 
6.0%
4 6377
 
2.4%
3 4065
 
1.6%
0 2724
 
1.0%
2 2239
 
0.9%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 2724
 
1.0%
1 919
 
0.4%
2 2239
 
0.9%
3 4065
 
1.6%
4 6377
 
2.4%
5 15607
 
6.0%
6 21380
 
8.2%
7 39286
15.1%
8 64527
24.8%
9 40266
15.5%
ValueCountFrequency (%)
10 43990
16.9%
9 40266
15.5%
8 64527
24.8%
7 39286
15.1%
6 21380
 
8.2%
5 15607
 
6.0%
4 6377
 
2.4%
3 4065
 
1.6%
2 2239
 
0.9%
1 919
 
0.4%

NPCJP9AJ
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean8.5484091
Minimum0
Maximum10
Zeros804
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:37.001367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median9
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5652275
Coefficient of variation (CV)0.18310161
Kurtosis4.305011
Mean8.5484091
Median Absolute Deviation (MAD)1
Skewness-1.6561225
Sum2063415
Variance2.449937
MonotonicityNot monotonic
2024-04-21T21:17:37.153671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 81351
31.2%
9 60685
23.3%
8 53545
20.6%
7 23674
 
9.1%
6 10850
 
4.2%
5 6278
 
2.4%
4 1960
 
0.8%
3 1174
 
0.5%
0 804
 
0.3%
2 751
 
0.3%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 804
 
0.3%
1 308
 
0.1%
2 751
 
0.3%
3 1174
 
0.5%
4 1960
 
0.8%
5 6278
 
2.4%
6 10850
 
4.2%
7 23674
 
9.1%
8 53545
20.6%
9 60685
23.3%
ValueCountFrequency (%)
10 81351
31.2%
9 60685
23.3%
8 53545
20.6%
7 23674
 
9.1%
6 10850
 
4.2%
5 6278
 
2.4%
4 1960
 
0.8%
3 1174
 
0.5%
2 751
 
0.3%
1 308
 
0.1%

NPCJP9AK
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean7.5123581
Minimum0
Maximum10
Zeros1543
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:37.308211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8231162
Coefficient of variation (CV)0.24268229
Kurtosis1.7939422
Mean7.5123581
Median Absolute Deviation (MAD)1
Skewness-1.0338426
Sum1813333
Variance3.3237528
MonotonicityNot monotonic
2024-04-21T21:17:37.475245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 70491
27.1%
7 48437
18.6%
9 34017
13.1%
10 33592
12.9%
6 24399
 
9.4%
5 16130
 
6.2%
4 6366
 
2.4%
3 3661
 
1.4%
2 2038
 
0.8%
0 1543
 
0.6%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 1543
 
0.6%
1 706
 
0.3%
2 2038
 
0.8%
3 3661
 
1.4%
4 6366
 
2.4%
5 16130
 
6.2%
6 24399
 
9.4%
7 48437
18.6%
8 70491
27.1%
9 34017
13.1%
ValueCountFrequency (%)
10 33592
12.9%
9 34017
13.1%
8 70491
27.1%
7 48437
18.6%
6 24399
 
9.4%
5 16130
 
6.2%
4 6366
 
2.4%
3 3661
 
1.4%
2 2038
 
0.8%
1 706
 
0.3%

NPCJP10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean7.7138495
Minimum0
Maximum10
Zeros2175
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:37.636958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.976945
Coefficient of variation (CV)0.25628514
Kurtosis1.7381357
Mean7.7138495
Median Absolute Deviation (MAD)1
Skewness-1.1579441
Sum1861969
Variance3.9083114
MonotonicityNot monotonic
2024-04-21T21:17:37.796125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 63208
24.3%
10 50126
19.2%
9 38737
14.9%
7 37085
14.2%
6 21105
 
8.1%
5 15040
 
5.8%
4 7264
 
2.8%
3 3521
 
1.4%
2 2249
 
0.9%
0 2175
 
0.8%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 2175
 
0.8%
1 870
 
0.3%
2 2249
 
0.9%
3 3521
 
1.4%
4 7264
 
2.8%
5 15040
 
5.8%
6 21105
 
8.1%
7 37085
14.2%
8 63208
24.3%
9 38737
14.9%
ValueCountFrequency (%)
10 50126
19.2%
9 38737
14.9%
8 63208
24.3%
7 37085
14.2%
6 21105
 
8.1%
5 15040
 
5.8%
4 7264
 
2.8%
3 3521
 
1.4%
2 2249
 
0.9%
1 870
 
0.3%

NPCJP11
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean3.3514334
Minimum0
Maximum10
Zeros69723
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:37.951314image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.0530161
Coefficient of variation (CV)0.9109583
Kurtosis-0.93340343
Mean3.3514334
Median Absolute Deviation (MAD)3
Skewness0.50563504
Sum808969
Variance9.3209072
MonotonicityNot monotonic
2024-04-21T21:17:38.113656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 69723
26.8%
2 25024
 
9.6%
3 23570
 
9.0%
5 20596
 
7.9%
4 20497
 
7.9%
1 17952
 
6.9%
7 16987
 
6.5%
8 16534
 
6.3%
6 16179
 
6.2%
10 8174
 
3.1%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 69723
26.8%
1 17952
 
6.9%
2 25024
 
9.6%
3 23570
 
9.0%
4 20497
 
7.9%
5 20596
 
7.9%
6 16179
 
6.2%
7 16987
 
6.5%
8 16534
 
6.3%
9 6144
 
2.4%
ValueCountFrequency (%)
10 8174
 
3.1%
9 6144
 
2.4%
8 16534
6.3%
7 16987
6.5%
6 16179
6.2%
5 20596
7.9%
4 20497
7.9%
3 23570
9.0%
2 25024
9.6%
1 17952
6.9%

NPCJP12
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean2.166182
Minimum0
Maximum10
Zeros107643
Zeros (%)41.3%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:38.270431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6551735
Coefficient of variation (CV)1.2257389
Kurtosis0.21438204
Mean2.166182
Median Absolute Deviation (MAD)1
Skewness1.1130471
Sum522873
Variance7.0499461
MonotonicityNot monotonic
2024-04-21T21:17:38.424748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 107643
41.3%
2 26629
 
10.2%
1 22804
 
8.8%
3 20483
 
7.9%
4 15344
 
5.9%
5 14311
 
5.5%
6 10051
 
3.9%
7 9730
 
3.7%
8 8580
 
3.3%
10 3130
 
1.2%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 107643
41.3%
1 22804
 
8.8%
2 26629
 
10.2%
3 20483
 
7.9%
4 15344
 
5.9%
5 14311
 
5.5%
6 10051
 
3.9%
7 9730
 
3.7%
8 8580
 
3.3%
9 2675
 
1.0%
ValueCountFrequency (%)
10 3130
 
1.2%
9 2675
 
1.0%
8 8580
 
3.3%
7 9730
 
3.7%
6 10051
 
3.9%
5 14311
5.5%
4 15344
5.9%
3 20483
7.9%
2 26629
10.2%
1 22804
8.8%

NPCJP13
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing19096
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean7.687733
Minimum0
Maximum10
Zeros1054
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:38.579120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median8
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6842482
Coefficient of variation (CV)0.21908256
Kurtosis2.1417346
Mean7.687733
Median Absolute Deviation (MAD)1
Skewness-1.0689523
Sum1855665
Variance2.836692
MonotonicityNot monotonic
2024-04-21T21:17:38.742275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 69859
26.8%
7 46773
18.0%
9 45809
17.6%
10 31858
12.2%
6 23338
 
9.0%
5 14160
 
5.4%
4 4421
 
1.7%
3 2339
 
0.9%
2 1223
 
0.5%
0 1054
 
0.4%
(Missing) 19096
 
7.3%
ValueCountFrequency (%)
0 1054
 
0.4%
1 546
 
0.2%
2 1223
 
0.5%
3 2339
 
0.9%
4 4421
 
1.7%
5 14160
 
5.4%
6 23338
 
9.0%
7 46773
18.0%
8 69859
26.8%
9 45809
17.6%
ValueCountFrequency (%)
10 31858
12.2%
9 45809
17.6%
8 69859
26.8%
7 46773
18.0%
6 23338
 
9.0%
5 14160
 
5.4%
4 4421
 
1.7%
3 2339
 
0.9%
2 1223
 
0.5%
1 546
 
0.2%

FEX_C
Text

Distinct65203
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2024-04-21T21:17:39.109993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.887721
Min length11

Characters and Unicode

Total characters4138370
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5975 ?
Unique (%)2.3%

Sample

1st row1,69849246231156
2nd row1,69849246231156
3rd row6,74441212899351
4th row6,74441212899351
5th row6,74441212899351
ValueCountFrequency (%)
1,80463242698892 1707
 
0.7%
1,13167701863354 1373
 
0.5%
1,4395777439874 543
 
0.2%
1,69849246231156 536
 
0.2%
1,1996481199895 379
 
0.1%
10,7610389527659 250
 
0.1%
29,4959010692373 178
 
0.1%
1,18106995884774 163
 
0.1%
1,30870703998855 130
 
< 0.1%
19,7627135909443 117
 
< 0.1%
Other values (65193) 255100
97.9%
2024-04-21T21:17:39.683597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 434031
10.5%
2 420161
10.2%
3 403218
9.7%
4 398997
9.6%
5 382238
9.2%
6 382028
9.2%
8 376893
9.1%
7 373705
9.0%
9 369923
8.9%
0 336700
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3877894
93.7%
Other Punctuation 260476
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 434031
11.2%
2 420161
10.8%
3 403218
10.4%
4 398997
10.3%
5 382238
9.9%
6 382028
9.9%
8 376893
9.7%
7 373705
9.6%
9 369923
9.5%
0 336700
8.7%
Other Punctuation
ValueCountFrequency (%)
, 260476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4138370
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 434031
10.5%
2 420161
10.2%
3 403218
9.7%
4 398997
9.6%
5 382238
9.2%
6 382028
9.2%
8 376893
9.1%
7 373705
9.0%
9 369923
8.9%
0 336700
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4138370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 434031
10.5%
2 420161
10.2%
3 403218
9.7%
4 398997
9.6%
5 382238
9.2%
6 382028
9.2%
8 376893
9.1%
7 373705
9.0%
9 369923
8.9%
0 336700
8.1%

Interactions

2024-04-21T21:17:04.180860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:07.015625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:09.934988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:12.818707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.634656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:18.210629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:21.077026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:23.622225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.256739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:29.116158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:31.940862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.511744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:37.125754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:40.145279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:43.121654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.635058image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:48.138598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:51.285561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:54.305918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:57.362115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:00.670871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:04.353549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:07.154066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:10.130204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:12.947556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.764214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:18.337390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:21.209383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:23.764084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.380604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:29.239843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:32.078285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.654016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:37.592421image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:40.272046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:43.241460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.757445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:48.261020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:51.413653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:54.452611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:57.524956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:00.831531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:04.533536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:07.285877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:10.260672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:13.066571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.881190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:18.458576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:21.330631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:23.896288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.494178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:29.359484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:32.204702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.779137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:37.726200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:40.398261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:43.366625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.879960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:48.383065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:51.549870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:54.596451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:57.682024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:00.994153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:04.703269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:07.409430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:10.396246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:13.218307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.990499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:18.595229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:21.449961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:24.022645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.608138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:29.501173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:32.325581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.905879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:37.851511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:40.524446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:43.497154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.996286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:48.498643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:51.683258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:54.737124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:57.835386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:01.153486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:04.870262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:07.537823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:10.530860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:13.348357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:16.105773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:18.736316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:21.566850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:24.172454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.747116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:29.636310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:32.446061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:35.035619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:37.974587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:40.648624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:43.618259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:46.113280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:48.618242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:51.808854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:54.879479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:57.980326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:01.313537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:05.047102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:07.660157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:10.678930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:13.479883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:16.224742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:18.878752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:21.680830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:24.331107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.866770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:29.771963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:32.576961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:35.166927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:38.107206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-04-21T21:16:17.442783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:20.344077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:22.874998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:25.527498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:28.401530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:31.166618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:33.790331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:36.352334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:39.368367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:42.295415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:44.920696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:47.426963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:50.553007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:53.462430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:56.461422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:59.731604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:03.188519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:07.069081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:09.188325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:12.181072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.012323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:17.558301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:20.458498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:22.992978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:25.642057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:28.519391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:31.299956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:33.910809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:36.480846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:39.502570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:42.434525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.033324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:47.544567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:50.673331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:53.598795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:56.609068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:59.887803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:03.352719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:07.241099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:09.311111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:12.320640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.137277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:17.681981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:20.573074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:23.108417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:25.753156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:28.637511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:31.434210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.039399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:36.600384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:39.643872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:42.583694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.148965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:47.663843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:50.793803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:53.742509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:56.746422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:00.046540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:03.519156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:07.410754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:09.459181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:12.451580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.267638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:17.810007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:20.687917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:23.226809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:25.871969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:28.750096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:31.566216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.160764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:36.716444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:39.779390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:42.719929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.280454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:47.785528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:50.915204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:53.882187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:56.894166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:00.194417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:03.686720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:07.586949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:09.598944image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:12.580372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.381436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:17.930443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:20.811980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:23.339514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.003033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:28.867742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:31.692368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.282936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:36.856808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:39.899990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:42.870172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.399943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:47.902306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:51.037592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:54.022204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:57.053566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:00.359076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:03.843409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:07.748711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:09.771848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:12.693938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:15.504625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:18.073139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:20.946019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:23.449871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:26.137362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:28.990376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:31.815721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:34.397456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:36.996953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:40.025105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:43.001254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:45.517492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:48.021703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:51.161900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:54.161458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:16:57.206298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:00.513101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-21T21:17:04.012821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-21T21:17:39.901120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
DIRECTORIODIRECTORIO_HOGDIRECTORIO_PERNPCJP10NPCJP11NPCJP12NPCJP13NPCJP2NPCJP3NPCJP6NPCJP7NPCJP9AANPCJP9ABNPCJP9ACNPCJP9ADNPCJP9AENPCJP9AFNPCJP9AGNPCJP9AHNPCJP9AINPCJP9AJNPCJP9AKORDENSECUENCIA_P
DIRECTORIO1.0001.0000.999-0.014-0.069-0.0080.1000.0590.0520.045-0.0580.0230.0170.1070.0220.0800.0770.046-0.0200.0530.0150.051-0.020-0.033
DIRECTORIO_HOG1.0001.0000.999-0.014-0.069-0.0080.1000.0590.0520.045-0.0580.0230.0170.1070.0220.0800.0770.046-0.0200.0530.0150.051-0.020-0.033
DIRECTORIO_PER0.9990.9991.000-0.014-0.069-0.0080.1000.0380.0450.037-0.0580.0230.0160.1070.0220.0800.0770.046-0.0210.0530.0150.051-0.019-0.033
NPCJP10-0.014-0.014-0.0141.000-0.398-0.3840.3800.0370.0610.0290.0670.5140.4440.2270.4570.2330.2350.4180.5010.4200.4550.430-0.000-0.002
NPCJP11-0.069-0.069-0.069-0.3981.0000.552-0.2220.0330.0500.020-0.021-0.260-0.232-0.153-0.233-0.121-0.165-0.222-0.216-0.204-0.184-0.208-0.0560.016
NPCJP12-0.008-0.008-0.008-0.3840.5521.000-0.1360.0480.0450.026-0.042-0.244-0.230-0.057-0.238-0.056-0.098-0.231-0.305-0.200-0.251-0.218-0.0070.006
NPCJP130.1000.1000.1000.380-0.222-0.1361.0000.0240.0560.015-0.0000.4480.3970.3680.3130.3030.3090.3230.2870.3860.3580.353-0.026-0.024
NPCJP20.0590.0590.0380.0370.0330.0480.0241.0001.0001.000NaN-0.0040.0050.012-0.042-0.0350.057-0.066-0.049-0.064-0.015-0.0010.0030.004
NPCJP30.0520.0520.0450.0610.0500.0450.0561.0001.0000.166NaN-0.054-0.051-0.080-0.024-0.128-0.033-0.082-0.030-0.108-0.065-0.0530.115-0.008
NPCJP60.0450.0450.0370.0290.0200.0260.0151.0000.1661.000NaN0.0280.0150.027-0.0030.035-0.0240.0080.0190.0350.0260.025-0.049-0.012
NPCJP7-0.058-0.058-0.0580.067-0.021-0.042-0.000NaNNaNNaN1.0000.0320.0400.0230.0480.050-0.0100.0480.0840.0300.0560.0530.0220.011
NPCJP9AA0.0230.0230.0230.514-0.260-0.2440.448-0.004-0.0540.0280.0321.0000.5760.3180.5050.2920.2820.4230.4790.4440.4720.411-0.008-0.006
NPCJP9AB0.0170.0170.0160.444-0.232-0.2300.3970.005-0.0510.0150.0400.5761.0000.3280.4370.2580.3000.4220.4590.4470.4180.4750.006-0.017
NPCJP9AC0.1070.1070.1070.227-0.153-0.0570.3680.012-0.0800.0270.0230.3180.3281.0000.2810.7030.3280.3100.1680.3610.2780.307-0.154-0.013
NPCJP9AD0.0220.0220.0220.457-0.233-0.2380.313-0.042-0.024-0.0030.0480.5050.4370.2811.0000.3180.2930.4630.4770.4380.4530.3900.0370.004
NPCJP9AE0.0800.0800.0800.233-0.121-0.0560.303-0.035-0.1280.0350.0500.2920.2580.7030.3181.0000.2950.3280.1890.3490.2990.283-0.156-0.007
NPCJP9AF0.0770.0770.0770.235-0.165-0.0980.3090.057-0.033-0.024-0.0100.2820.3000.3280.2930.2951.0000.3920.2080.3270.2500.4560.024-0.006
NPCJP9AG0.0460.0460.0460.418-0.222-0.2310.323-0.066-0.0820.0080.0480.4230.4220.3100.4630.3280.3921.0000.5010.4820.4350.4830.026-0.004
NPCJP9AH-0.020-0.020-0.0210.501-0.216-0.3050.287-0.049-0.0300.0190.0840.4790.4590.1680.4770.1890.2080.5011.0000.4340.5310.4280.010-0.001
NPCJP9AI0.0530.0530.0530.420-0.204-0.2000.386-0.064-0.1080.0350.0300.4440.4470.3610.4380.3490.3270.4820.4341.0000.4700.4630.012-0.011
NPCJP9AJ0.0150.0150.0150.455-0.184-0.2510.358-0.015-0.0650.0260.0560.4720.4180.2780.4530.2990.2500.4350.5310.4701.0000.427-0.084-0.006
NPCJP9AK0.0510.0510.0510.430-0.208-0.2180.353-0.001-0.0530.0250.0530.4110.4750.3070.3900.2830.4560.4830.4280.4630.4271.000-0.026-0.007
ORDEN-0.020-0.020-0.019-0.000-0.056-0.007-0.0260.0030.115-0.0490.022-0.0080.006-0.1540.037-0.1560.0240.0260.0100.012-0.084-0.0261.000-0.014
SECUENCIA_P-0.033-0.033-0.033-0.0020.0160.006-0.0240.004-0.008-0.0120.011-0.006-0.017-0.0130.004-0.007-0.006-0.004-0.001-0.011-0.006-0.007-0.0141.000

Missing values

2024-04-21T21:17:08.667982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:17:10.761928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DIRECTORIODIRECTORIO_HOGDIRECTORIO_PERSECUENCIA_PORDENNPCJP1BNPCJP1CNPCJP1DNPCJP1ENPCJP1FNPCJP1GNPCJP1HNPCJP1INPCJP1JNPCJP1KNPCJP1LNPCJP1MNPCJP1NNPCJP1ANPCJP1ONPCJP1PNPCJP1QNPCJP2NPCJP3NPCJP6NPCJP7NPCJP9ANPCJP9BNPCJP9CNPCJP9DNPCJP9ENPCJP9FNPCJP9GNPCJP9HNPCJP9INPCJP9JNPCJP8ANPCJP8BNPCJP8CNPCJP8DNPCJP8ENPCJP8FNPCJP8GNPCJP8HNPCJP9AANPCJP9ABNPCJP9ACNPCJP9ADNPCJP9AENPCJP9AFNPCJP9AGNPCJP9AHNPCJP9AINPCJP9AJNPCJP9AKNPCJP10NPCJP11NPCJP12NPCJP13FEX_C
0166238.016623811662381111NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN2.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaN8.08.08.08.08.08.08.08.08.09.08.07.08.07.08.01,69849246231156
1166238.016623811662381212NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN2.0NaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaN9.09.09.09.08.09.08.08.08.08.07.08.07.07.08.01,69849246231156
2220102.022010212201021111NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN9.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaN1.0NaN6.05.04.06.06.08.09.09.06.07.010.07.06.01.07.06,74441212899351
3220102.022010212201021212NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaN7.0NaNNaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaN1.0NaN7.05.02.06.02.05.07.08.08.04.010.08.01.02.08.06,74441212899351
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